Why Python, and not another programming language?
Why do experienced software developers like to recommend Python? Why has Python become one of the world’s most popular programming languages?
The answer is simple.
Thanks to its elegance and simplicity. It also has a lot of other advantages when we compare it with other programming languages.
It is used in everything from machine learning to building websites and software testing. Everyone can use it - developers and non-developers as well. For over 30 years, Python has remained a popular choice among numerous companies and organizations.
The motto of Python is:
Simplicity is the key
Python is incredibly easy to use and learn. Simplicity in the syntax makes it easy to read and understand (also by newcomers). It has simplified syntax and emphasizes natural language, hence wide adoption in academics.
Python is an open-source language, meaning it is free, can be used by anyone, and the internet is filled with a tremendous amount of resources from which you can learn. Also, Python has an excellent and well-organized set of libraries that you can use in your work and save time and effort.
Python’s extensibility refers to the possibility of being extended to other languages. It allows writing Python modules in other languages and interface libraries written in different languages.
The versatility of Python means that it can be used in many environments like desktop applications, web development, and hardware programming. Furthermore, it allows developers to find their niche and select one, two, or more in which they feel the best.
Python was created over 30 years ago, which gave it a lot of time to develop and grow the community of developers ranging from beginners to experts. The positive outcomes are specific documentation, guides, and tutorials for learners available at your fingertips. As a result, the Python developer community is one of the most active, with people ready to share their knowledge and give almost instant support.
Disadvantages of using Python
Since there is no doubt about Python’s popularity (reasons mentioned earlier), like everything else, Python also has its weaknesses.
The main disadvantage of Python is its slowness during execution. Besides that:
- difficulty in switching to another programming language
- weak in mobile app development
- high memory consumption
Let’s take a closer look at each of them.
Python is slow at Runtime
Regarding speed, we must remember that this is a focal point for the programmers. Python is an interpreted language and is slow compared to C/C++ or Java. Unlike C or C++, it’s not closer to hardware because it is a higher-level language.
Python code execution takes place with the help of an interpreter (not a compiler). The interpreter executes pre-compiled byte-code like it is with other languages (e.g., Java, Ruby, C#), which obviously impacts performance.
The major drawback is the language design. Because the code is executed dynamically, some of the errors only appear at runtime (while static programming languages perform that kind of errors during compilation) - the language requires more testing. You can mitigate this problem by using linting tools and other static analyzers.
Still, it is very reasonable to write your code in Python because even though it is not the fastest language to run, it is still the fastest to write. These days in the cloud era, where you can employ a cluster of computers to do a task, its execution speed becomes less relevant.
Python is not great for mobile application development
Python works best on desktop and server platforms; it is an excellent server-side language. However, it is not a perfect choice for mobile development.
Python has high memory consumption
Python’s memory consumption is high due to the flexibility of the data types. In addition, Python has the functionality of automatic garbage collection when objects go out of scope. Due to this, Python removes much of the memory management complexity that languages like C and C++ involve.
The advice is that before you write Python code for highly memory-intensive tasks, try to understand the space and efficiency of the code and the underlying packages used.
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